---
language:
- multilingual
tags:
- translation
- quality-estimation
- claude-haiku
license: mit
---

# natgillin/translations — Claude-Haiku-filtered bitext

Rows from `natgillin/translations-raw` that scored `> 0.8` on Claude Haiku 4.5 translation-quality evaluation. Globally deduplicated by xxh3-64 of `source\ntarget`.

## Schema

Each parquet file has 11 columns:

| column | type | description |
|---|---|---|
| `source` | string | source-language sentence |
| `target` | string | target-language sentence |
| `source_lang` | string | ISO-639-3 source language code (from upstream metadata) |
| `target_lang` | string | ISO-639-3 target language code (from upstream metadata) |
| `origin` | string | upstream OPUS corpus tag (e.g. `opus-nllb`) |
| `xxhash_intdigest` | uint64 | hash of the pair (see below) |
| `claude_haiku_score` | float32 | quality score in `[0.0, 1.0]` from Claude Haiku 4.5 |
| `source_lang_claude_haiku_detect` | string | ISO-639-3 of language Haiku detected in `source` (or `unk`) |
| `source_lang_claude_haiku_detect_confidence` | float32 | Haiku's self-reported confidence in `[0.0, 1.0]` for the source-lang detection |
| `target_lang_claude_haiku_detect` | string | ISO-639-3 of language Haiku detected in `target` (or `unk`) |
| `target_lang_claude_haiku_detect_confidence` | float32 | Haiku's self-reported confidence in `[0.0, 1.0]` for the target-lang detection |

## Keep / reject criteria

A row is **kept** (in `natgillin/translations`) when **all** hold:

1. `claude_haiku_score > 0.8`
2. NOT (`source_lang_claude_haiku_detect != source_lang` AND `source_lang_claude_haiku_detect_confidence >= 0.70`)
3. NOT (`target_lang_claude_haiku_detect != target_lang` AND `target_lang_claude_haiku_detect_confidence >= 0.70`)

I.e. a language mismatch only rejects a row when Haiku is **confident** about the mismatch (`>=0.70`). Low-confidence detections (typical for low-resource languages like Aymara, Quechua, Guarani) do NOT auto-reject, since Haiku's training has thin coverage there. Both labels and their confidences are stored on every row so downstream consumers can re-derive the split.

## How `xxhash_intdigest` is computed

```python
import xxhash

def row_hash(source: str, target: str) -> int:
    return xxhash.xxh3_64(f"{source}\n{target}".encode("utf-8")).intdigest()
```

The hash is the **xxh3-64** intdigest of `f"{source}\n{target}"` encoded as UTF-8.
It is stable across runs and lets you deduplicate or join rows by content.

## How `claude_haiku_score` + lang-detect columns are computed

Rows are batched and sent to Claude Haiku 4.5 with the prompt below. For each pair,
the model returns three values:

- `s`: quality score in `[0.0, 1.0]` → stored as `claude_haiku_score`
- `sl`: detected source language (ISO-639-3) → stored as `source_lang_claude_haiku_detect`
- `tl`: detected target language (ISO-639-3) → stored as `target_lang_claude_haiku_detect`

### Exact prompt template

```
You are a translation judge. For each pair, return FIVE values:
- s: fluency+faithfulness score 0.00-1.00 (1=perfect, 0.8=cutoff, 0.6=awkward, 0.4=broken, 0.2=garbled, 0=empty/wrong)
- sl: detected language of SRC text (ISO-639-3, 3 lowercase letters, or 'unk' if unsure)
- slp: confidence 0.00-1.00 that `sl` is correct (low if you're guessing or the language is low-resource like aym/que/grn)
- tl: detected language of TGT text (ISO-639-3, 3 lowercase letters, or 'unk')
- tlp: confidence 0.00-1.00 for `tl`

Metadata claims: source={src_lang}, target={tgt_lang}. Use the actual language you observe, not the metadata. Report your true confidence — use slp/tlp < 0.5 when the language is hard to identify rather than guessing.

Pairs:
{pairs_block}

Return ONLY this JSON (no prose), exactly {n} items in order:
{{"results":[{{"s":0.95,"sl":"aym","slp":0.4,"tl":"eng","tlp":0.99}}, ...]}}
```

### JSON schema sent alongside the prompt

```json
{
  "type": "object",
  "properties": {
    "scores": {
      "type": "array",
      "items": {
        "type": "number",
        "minimum": 0.0,
        "maximum": 1.0
      }
    }
  },
  "required": [
    "scores"
  ],
  "additionalProperties": false
}
```

## Source

All rows come from `natgillin/translations-raw` (an OPUS / mtdata mirror).
Before any Haiku call, rows are deduplicated globally by `xxhash_intdigest`
so identical pairs are scored exactly once.